The high energetic cost of rapid force development in cyclic muscle contraction
Bibliographic record
Abstract
Abstract Muscles consume metabolic energy for active movement, particularly when performing mechanical work or producing force. Less appreciated is the cost for activating and deactivating muscle quickly, which adds considerably to the overall cost of cyclic force production (Chasiotis et al., 1987). But the cost relative to mechanical work, which features in many movements, is unknown. We therefore tested whether fast activation-deactivation is costly compared to performing work or producing isometric force. We hypothesized that metabolic cost would increase with a proposed measure termed force-rate (rate of increase in muscle force) in cyclic tasks, separate from mechanical work or average force level. We tested humans (N = 9) producing cyclic knee extension torque against an isometric dynamometer (torque 22 N-m, cyclic waveform frequencies 0.5 – 2.5 Hz), while also quantifying the force and work of muscle fascicles against series elasticity (with ultrasonography), along with metabolic rate through respirometry. Net metabolic rate increased by more than fourfold (10.5 to 46.7 W) with waveform frequency. At high frequencies, the hypothesized force-rate cost accounted for nearly half (41%) of energy expenditure. This exceeded the cost for average force (17%) and was comparable to the cost for shortening work (42%). The energetic cost is explained by a simple first-order model of rate-limiting steps in muscle contraction, primarily crossbridge dynamics. The force-rate cost could contribute substantially to the overall cost of movements that require cyclic muscle activation, such as locomotion. Summary statement The energetic cost of isometric muscle force production during cyclic muscle contraction increases sharply with cycle frequency and in proportion to the rate of force development
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".